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‎.nojekyll‎

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<span class="navbar-title">Spatial Data Science across Languages</span>
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<title>Spatial Data Science across Languages Community – Spatial Data Science across Languages</title>
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<title>Spatial Data Science across Languages</title>
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<h2 id="toc-title">On this page</h2>
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<li><a href="#annual-workshop" id="toc-annual-workshop" class="nav-link active" data-scroll-target="#annual-workshop">Annual Workshop</a></li>
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<h1 class="title">Spatial Data Science across Languages Community</h1>
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<p><img src="img/sdsl-logo.svg" class="img-fluid"></p>
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<p>The Spatial Data Science across Languages Community brings together developers and users from the common and emerging programming languages used for spatial data science.</p>
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<p>Spatial data science (SDS) concerns the analysis of spatial data in various contexts. We focus broadly on geospatial and geographic space, with some applications to general image spaces, local reference frames - everything from microscopical to astronomical space.</p>
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<p>Open source programming languages commonly used in spatial data science for analysis include Python, R and Julia. Our community is also interested in JavaScript and TypeScript, C++ and Rust. These languages are used by millions of users on a daily basis to solve spatial data problems, visualise and analyse spatial data.</p>
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<p>A number of the challenges that we face transcend the particular programming languages. Such challenges range from: the interpretation of the underlying data; the way the data are represented in computers; visualisation; scalability and efficiency of implementations; the use of upstream libraries like GDAL, GEOS and PROJ, GIS interfaces; software distribution; and open source software community building.</p>
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<p>The <strong>Spatial Data Science Across Language Community</strong> aims to bring developers and users together to help build understanding and solve common problems, as well as discussing problems specific to particular language communities.</p>
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<p>The <strong>Spatial Data Science Across Languages Community</strong> aims to bring developers and users together to help build understanding and solve common problems, as well as discussing problems specific to particular language communities.</p>
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<section id="annual-workshop" class="level2">
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<h2 class="anchored" data-anchor-id="annual-workshop">Annual Workshop</h2>
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<p>Our main activity since 2023 has been a series of annual workshops, which provide a space for bridging the various programming-language communities and establishing cross-language interaction between developers and users.</p>

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<li><a href="#spatial-data-science-languages-commonalities-and-needs" id="toc-spatial-data-science-languages-commonalities-and-needs" class="nav-link active" data-scroll-target="#spatial-data-science-languages-commonalities-and-needs">Spatial Data Science Languages: Commonalities and Needs</a></li>
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<section id="spatial-data-science-languages-commonalities-and-needs" class="level2">
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<h2 class="anchored" data-anchor-id="spatial-data-science-languages-commonalities-and-needs">Spatial Data Science Languages: Commonalities and Needs</h2>
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<p>The report from the SDSL workshops 2023 and 2024 has been published as an academic paper available from <a href="https://arxiv.org/abs/2503.16686" class="uri">https://arxiv.org/abs/2503.16686</a>.</p>
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<p><em>Pebesma, E., Fleischmann, M., Parry, J., Nowosad, J., Graser, A., Dunnington, D., Pronk, M., Schouten, R., Lovelace, R., Appel, M., Abad, L., 2025. Spatial Data Science Languages: commonalities and needs. https://doi.org/10.48550/arXiv.2503.16686</em></p>
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<blockquote class="blockquote">
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<p>Recent workshops brought together several developers, educators and users of software packages extending popular languages for spatial data handling, with a primary focus on R, Python and Julia. Common challenges discussed included handling of spatial or spatio-temporal support, geodetic coordinates, in-memory vector data formats, data cubes, inter-package dependencies, packaging upstream libraries, differences in habits or conventions between the GIS and physical modelling communities, and statistical models. The following set of insights have been formulated: (i) considering software problems across data science language silos helps to understand and standardise analysis approaches, also outside the domain of formal standardisation bodies; (ii) whether attribute variables have block or point support, and whether they are spatially intensive or extensive has consequences for permitted operations, and hence for software implementing those; (iii) handling geometries on the sphere rather than on the flat plane requires modifications to the logic of simple features, (iv) managing communities and fostering diversity is a necessary, on-going effort, and (v) tools for cross-language development need more attention and support.</p>
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<div class="code-copy-outer-scaffold"><div class="sourceCode" id="cb1"><pre class="sourceCode bibtex code-with-copy"><code class="sourceCode bibtex"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="va">@misc</span>{<span class="ot">pebesma2025Spatial</span>,</span>
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<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a> <span class="dt">title</span> = {Spatial {{Data Science Languages}}: Commonalities and Needs},</span>
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<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a> <span class="dt">shorttitle</span> = {Spatial {{Data Science Languages}}},</span>
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<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a> <span class="dt">author</span> = {Pebesma, Edzer and Fleischmann, Martin and Parry, Josiah and Nowosad, Jakub and Graser, Anita and Dunnington, Dewey and Pronk, Maarten and Schouten, Rafael and Lovelace, Robin and Appel, Marius and Abad, Lorena},</span>
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<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a> <span class="dt">year</span> = {2025},</span>
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<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a> <span class="dt">month</span> = <span class="st">mar</span>,</span>
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<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a> <span class="dt">number</span> = {arXiv:2503.16686},</span>
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<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a> <span class="dt">eprint</span> = {2503.16686},</span>
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<span id="cb1-9"><a href="#cb1-9" aria-hidden="true" tabindex="-1"></a> <span class="dt">primaryclass</span> = {stat},</span>
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<span id="cb1-10"><a href="#cb1-10" aria-hidden="true" tabindex="-1"></a> <span class="dt">publisher</span> = {arXiv},</span>
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<span id="cb1-11"><a href="#cb1-11" aria-hidden="true" tabindex="-1"></a> <span class="dt">doi</span> = {10.48550/arXiv.2503.16686},</span>
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<span id="cb1-12"><a href="#cb1-12" aria-hidden="true" tabindex="-1"></a> <span class="dt">urldate</span> = {2025-09-19},</span>
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<span id="cb1-13"><a href="#cb1-13" aria-hidden="true" tabindex="-1"></a> <span class="dt">abstract</span> = {},</span>
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<span id="cb1-14"><a href="#cb1-14" aria-hidden="true" tabindex="-1"></a> <span class="dt">archiveprefix</span> = {arXiv},</span>
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<span id="cb1-15"><a href="#cb1-15" aria-hidden="true" tabindex="-1"></a> <span class="dt">keywords</span> = {Computer Science - Programming Languages,Statistics - Computation}</span>
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<span id="cb1-16"><a href="#cb1-16" aria-hidden="true" tabindex="-1"></a>}</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
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